Abstract
Semantic image segmentation plays a crucial role in scene understanding tasks. In autonomous driving, the driving of the vehicle causes the scale changes of objects in the street scene. Although multi-scale features can be learned through concatenating multiple different atrous-convolved features, it is difficult to accurately segment pedestrians with only partial feature information due to factors such as occlusion. Therefore, we propose a Xiphoid Spatial Pyramid Pooling method integrated with detailed information. This method, while connecting the features of multiple atrous-convolved, retains the image-level features of target boundary information. Based on the above methods, we design an encoder-decoder architecture called DXNet. The encoder is composed of a deep convolution neural network and two XSPP modules, and the decoder decodes the advanced features through up-sampling operation and skips connection to gradually restore the target boundary. We evaluate the effectiveness of our approach on the Cityscapes dataset. Experimental results show that our method performs better in the case of occlusion, and the mean intersection-over-union score of our model outperforms some representative works.
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Acknowledgments
The authors would like to thank the anonymous reviewers for their helpful and constructive comments. This work was partially supported by the National Natural Science Foundation of China (NSFC Grant No. 61972059, 61702055, 61773272, 61272059) Natural Science Foundation of Jiangsu Province under Grant (BK20191474, BK20161268). Research and Innovation Fund of the Science and Technology Development Center of the Ministry of Education (2018A01007), and Ministry of Education Science and Technology Development Center Industry-University Research Innovation Fund (2018A02003), and Humanities and Social Sciences Foundation of the Ministry of Education under Grant 18YJCZH229.
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Shang, Y., Zhong, S., Gong, S., Zhou, L., Ying, W. (2019). DXNet: An Encoder-Decoder Architecture with XSPP for Semantic Image Segmentation in Street Scenes. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_59
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DOI: https://doi.org/10.1007/978-3-030-36802-9_59
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